1. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. AMESD eStation users’ training N 08. Precipitations products from RS Marco Clerici, JRC/IES/GEM

2. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. TAMSAT product (UK) FEWSNET product (USA) MPE product (EUMETSAT) Contents

3. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. TAMSAT Operational Rainfall Monitoring for Africa David Grimes TAMSAT* Dept of Meteorology University of Reading U.K. TAMSAT = Tropical Applications of Meteorology using SATellite data

4. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. Use Meteosat TIR imagery Identify cloud top temperature threshold T t distinguishing between rain and no rain Calculate Cold Cloud Duration (CCD) for each pixel (length of time cloud top is colder than T t ) Estimate rain amount as rain = a 0 + a 1 CCD a 0, a 1, T t are calibrated against local gauges using historic data Calibration parameters vary in space and time TAMSAT algorithm

5. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. TIR image 18:00, 23/07/08

6. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. 19/09/2015 Comparison of Satellite rainfall estimates and gauge data over the Sahel July dekad 2 2004 Met office – Raingauge anomaly TAMSAT – Raingauge anomaly Met office – TAMSAT anomaly

7. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. TAMSAT Calibration zones August Calibration zones vary slightly from month to month Differently shaded areas show the different zones for August

8. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. TAMSAT operational estimates No of gauges for calibration = 4569 Format: idrisi or geotiff Projection = lat long Nominal resolution = 0.0375 0

9. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. 9 Dekad 2 August, 2009

10. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. Validation of rainfall estimates Question: How do we know if rainfall estimates are any good? Answer: compare against independent data set. For Africa, this usually means comparison against raingauge data

11. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. + Use of gauge measurements for validation Validation in Africa is problematic because of the lack of ground-based observations JRC in collaboration with IPWG have produced a set of guidelines for African validation Main recommendations: –use geostatistical methods to convert gauge data to pixel (or larger) scale –specify minimum number of gauges per grid square –take account of uncertainty of gauge data as areal estimator gauges = + + + + +

12. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. Improved calibration Calibration currently being updated and extended as part of the MARSOP3 project Additional raingauge data provided by Meteoconsult Old calibration New calibration

13. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. Continental product

14. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. TAMSAT: Conclusions TAMSAT algorithm provides good quality rainfall estimates for most of Africa The approach is successful because of careful calibration against local gauge data Current calibrations are being extended to cover all Africa + Arabian peninsula for all months Operational products being used by JRC, Agrhymet, Uganda, Sudan, Ethiopia Current research includes –30 year time climatology and time series –improved algorithm using all MSG channels –ensemble estimation of uncertainty –applications to crop yield and hydrology

15. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. The Climate Prediction Center Rainfall Estimation Algorithm Version 2 Tim Love -- RSIS/CPC

16. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. RFE 2.0 Overview Run daily at NOAA CPC for Africa, southern Asia, Afghanistan area domains The Overall schema is: 1.Use satellite IR temperature data (MSG) for the GOES Precipitation Estimate (GPI) 2.gauge fields (via GTS) 3.Use microwave precip estimates (SSM/I & AMSU- B), 4.Combine the above products into RFE 2.0

17. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. Meteosat Data –Resultant field = cold cloud duration (CCD) @ 0.1° resolution (about 10 km) –CCD used for GOES Precipitation Index (GPI) calculation –GPI tends to overestimate spatial distribution but underestimates convective precipitation

18. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. GPI Estimate CPC RFE 2.0

19. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. GTS Data 2534 stations available daily Only 400-800 report daily Few reports from Nigeria, none from Liberia, Sierra Leone Data ingested from GTS line, Quality Controlled, fed to operational machine, then gridded to 0.1° resolution file Other station data may be readily used as input to algorithm via changing 2 tables in base code Requirements for RFE processing: –GPI and GTS inputs GTS = Global Telecommunication System

20. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. GTS Inputs

21. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. GTS vs GPI

22. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. SSM/I: definition Special Sensor Microwave/Imager a seven-channel, four-frequency, linearly polarized passive microwave radiometric system The instrument is flown onboard the US Air Force DMSP spacecraft

23. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. SSM/I Inputs 2 instruments estimate precip twice daily ~6 hourly data frequency Fails to catch other rainfall in temporal gaps Data needs only small conversion in preparation for input to algorithm

24. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. AMSU-B definition Advanced Microwave Sounding Unit 15-channel microwave radiometer installed on NOAA polar orbiting satellites.

25. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. As with SSM/I, data is available 4 times daily, staggered temporally Tends to overestimate most precip, but does well with highly convective systems Data sent in HDF format, thus needs to be deciphered before input to RFE algorithm Preprocessing straightforward AMSU-B Data

26. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. AMSU-B Estimate

27. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training.

28. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. Combining Satellite Estimates Combined analysis is a linear combination of each satellite estimate Satellite rainfall estimates are weighted by 1 / error variance Output dataset is then input to merging algorithm Estimates combined for all 6 resolutions, all satellite inputs Combined output =

29. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. TAMSAT – FEWSNET 2nd dekad Nov 2009

30. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. Multi-sensor Precipitation Estimate (MPE): An operational real-time rain-rate product Thomas Heinemann Meteorological Operations Division EUMETSAT thomas.heinemann@eumetsat.int

31. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. Geostationary satellite data: METEOSAT IR - data from the operational METEOSAT satellites: 0° (currently MET-9) and 57° East (currently MET-7) and RSS (currently MET-8) Polar orbiting satellite data: SSM/I and SSMIS passive microwave data from currently 2-3 of the American DMSP satellites on a sun-synchronous orbit: DMSP13: (SSM/I) DMSP15: (SSM/I), when undisturbed DMSP16: (SSMIS), pre-processed with SSMISPP MPE Algorithm : used data

32. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. SSM/I data Rain Rate (mm/h) METEOSAT data IR Brightness Temperature Longitude Latitude Store in the corresponding geographical box during a certain period of accumulation 2 IR Brightness Temperature(METEOSAT) Rain rate (mm/h) LUTs build 3 Create LUT (RR,T IR ) Co-located data 1 Temporal and spatial co- location Derive the product on IR- pixel level 4 Algorithm overview

33. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. MPE: a real-time precipitation algorithm

34. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. Validation against radar

35. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. Precipitation intensity & soil erosion / degradation

36. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. Impact of rain It is estimated that the erosion impact on the global scale is between 15 to 30 t/ha/yr, which equals 1 to 2 mm/yr soil loss. As a reference: Introduction à la gestion conservatoire de l'eau, de la biomasse » (http://www.fao.org/docrep/T1765F/t1765f0d.htm)

37. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. Relation between precipitation rates and Ek

38. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. Influence of season, max intensity in 30 minutes, and rains during preceding dekad (h 10 jours = soil humidity index) on erosion and water flow due by similar rain amounts on bare and vegetation covered soils (Roose, 1973)

39. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. Indonesia: I=86.517*D –0.408 I: intensity P (mm/day), D: rain duration (nb days) Problems if P > 80 mm/j for 3-5 days sustained rains Azores: I=144.06*D –0.5551 problems if 78 > P > 144 mm/j for 1-3 days Relations between Intensity and Duration

40. Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. Rainfall erosion power index Disaster Prevention System Department, Fukken Co, Japan 72 h 1.5h